A capacity automatic detection device and method for new energy capacitor
By establishing a gradient temperature environment and intelligent decoupling model in new energy capacitors, the problems of temperature misjudgment and electrode aging in capacity testing were solved, and higher testing accuracy was achieved.
Patent Information
- Application Number
- CN202510670792.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-23
- Publication Date
- 2026-02-17
- Estimated Expiration
- 2045-05-23
AI Technical Summary
Existing methods for testing the capacity of new energy capacitors suffer from misjudgments due to temperature changes, electrode aging, and dynamic coupling errors of parameters, resulting in inaccurate testing accuracy.
By establishing a gradient temperature environment, a capacity-temperature mapping model is generated, surface temperature distribution and charge/discharge data are obtained, temperature and aging coefficient compensation is constructed, and a smart decoupling model is built using multi-physics field joint parameters to decouple dynamic coupling parameters and perform capacity value compensation.
It improves the accuracy of new energy capacitor capacity testing, overcomes the effects of temperature changes and electrode aging, and reduces systematic errors.
Smart Images
Figure CN120385876B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of measuring electrical variables, and in particular to an automatic capacity detection device and method for new energy capacitors. Background Technology
[0002] New energy capacitors refer to capacitors used in the new energy field, primarily for energy storage and energy conversion. Common applications of new energy capacitors include energy storage systems and power balancing. They are used to store and release energy, bridging the gap between energy generation and consumption, and improving system stability and reliability. Therefore, it is necessary to test the capacity of new energy capacitors to ensure that the designed and actual capacitance values meet requirements.
[0003] In existing technologies, the capacitance of new energy capacitors is calculated directly from the circuit, neglecting various influencing factors that arise during charging and discharging. For example, in non-constant temperature environments, the capacitance changes drastically with temperature, leading to misjudgments. Furthermore, existing testing equipment exhibits dynamic coupling between various parameters, such as internal resistance, leakage current, and polarization voltage, resulting in systematic errors. Additionally, traditional capacitance testing methods may misinterpret electrode aging as normal capacitance degradation, leading to inaccurate testing accuracy. Summary of the Invention
[0004] The purpose of this invention is to provide an automatic capacity detection device and method for new energy capacitors, so as to solve the problems mentioned in the background art.
[0005] In a first aspect, this application provides an automatic capacity detection device for new energy capacitors, the device comprising:
[0006] Temperature analysis module: used to establish a gradient temperature environment, perform capacitance detection on the capacitor according to the gradient temperature environment, obtain the capacitance change law under different temperature conditions, and establish a capacitance-temperature mapping model based on the capacitance change law;
[0007] Temperature compensation module: used to acquire the surface temperature distribution of the capacitor during the detection process, and generate temperature coefficient compensation based on the capacitance-temperature mapping model and the surface temperature distribution;
[0008] Aging analysis module: used to acquire the charging and discharging data of the capacitor during the testing process, obtain the charging and discharging curve characteristics based on the charging and discharging data, and capture the voltage plateau slope change and relaxation time shift based on the charging and discharging curve characteristics.
[0009] Aging compensation module: used to obtain the electrode aging parameters of the capacitor based on the change in the slope of the voltage platform and the relaxation time offset, and to generate an aging coefficient compensation for capacity detection based on the electrode aging parameters;
[0010] Coupling Analysis Module: Used to acquire voltage data, current data, temperature data and frequency response of capacitor during charging and discharging process; generate multi-physics joint parameters based on the voltage data, current data, temperature data and frequency response; construct nonlinear regression equation based on the multi-physics joint parameters; and construct intelligent decoupling model based on the nonlinear regression equation.
[0011] Parameter decoupling module: used to acquire the dynamic coupling parameters between the capacitance value, internal resistance value, leakage current and polarization voltage of the capacitor during the detection process, and substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling to obtain the decoupled capacitance value;
[0012] Capacity calculation module: used to perform coefficient compensation on the decoupled capacity value based on the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacity value.
[0013] Preferably, the step of measuring the capacitance of the capacitor according to the gradient temperature environment to obtain the capacitance change pattern under different temperature conditions, and establishing a capacitance-temperature mapping model based on the capacitance change pattern, specifically includes:
[0014] Based on the gradient temperature environment, the capacitor is placed in different preset temperature environments to perform initial capacity testing and obtain multiple initial capacity data.
[0015] Based on the gradient temperature environment and multiple initial capacity data, a temperature-capacity correspondence table is generated, and the capacity change pattern is obtained based on the temperature-capacity correspondence table.
[0016] Based on the aforementioned capacity change pattern, a capacity-temperature curve is established, and the curve change characteristics are obtained from the capacity-temperature curve.
[0017] Based on the curve variation characteristics, the variation characteristics and corresponding relationships of capacity with temperature are obtained, and a capacity-temperature mapping model is established based on the variation characteristics and corresponding relationships.
[0018] Preferably, the step of acquiring the surface temperature distribution of the capacitor during the detection process and generating a temperature coefficient compensation based on the capacitance-temperature mapping model and the surface temperature distribution specifically includes:
[0019] The surface temperature distribution of the capacitor is obtained during the detection process, and a temperature distribution display map is generated based on the surface temperature distribution.
[0020] Based on the temperature distribution display map, the high-temperature and low-temperature regions on the capacitor surface, as well as the high-temperature and low-temperature values, are extracted.
[0021] Based on the high-temperature region and the low-temperature region, the corresponding regional temperature influence is obtained. The high-temperature value and the low-temperature value are weighted according to the regional influence to obtain the weighted high-temperature value and the weighted low-temperature value.
[0022] Based on the weighted high temperature value and the weighted low temperature value, a weighted average temperature value is generated, and the weighted average temperature value is substituted into the capacity-temperature mapping model to generate a temperature coefficient compensation.
[0023] Preferably, the steps of obtaining charge-discharge curve features based on the charge-discharge data, and capturing the voltage plateau slope change and relaxation time shift based on the charge-discharge curve features, specifically include:
[0024] A charge-discharge data curve is generated based on the charge-discharge data, and features are extracted from the charge-discharge data curve to obtain charge-discharge curve features.
[0025] Based on the characteristics of the charge-discharge curve, the voltage plateau data and relaxation time data of the capacitor are captured.
[0026] Based on the voltage platform data, the voltage platform slope data is obtained, and based on the voltage platform slope data, the voltage platform slope change is obtained;
[0027] The relaxation time offset is obtained by extracting the relaxation time offset based on the relaxation time data.
[0028] Preferably, the step of obtaining the capacitor's electrode aging parameters based on the voltage plateau slope change and the relaxation time offset, and generating the aging coefficient compensation for capacitance detection based on the electrode aging parameters, specifically includes:
[0029] The aging degree of the capacitor is identified by the voltage plateau slope data, and the first aging parameter of the capacitor is obtained.
[0030] The aging degree of the capacitor is identified by a second parameter based on the relaxation time offset, and a second aging parameter for the capacitor is obtained.
[0031] The first identification aging parameter and the second identification aging parameter are integrated to obtain the electrode aging parameters of the capacitor.
[0032] Based on the electrode aging parameters, the aging capacity impact value of electrode aging on capacitor capacity is obtained, and an aging coefficient compensation is generated based on the aging capacity impact value.
[0033] Preferably, the steps of generating multi-physics joint parameters based on the voltage data, the current data, the temperature data, and the frequency response; constructing a nonlinear regression equation based on the multi-physics joint parameters; and constructing an intelligent decoupling model based on the nonlinear regression equation are as follows:
[0034] Voltage physical field parameters are generated based on the voltage data, current physical field parameters are generated based on the current data, temperature physical field parameters are generated based on the temperature data, and frequency physical field parameters are generated based on the frequency response.
[0035] The physical field parameters are generated by combining the voltage physical field parameters, the current physical field parameters, the temperature physical field parameters, and the frequency physical field parameters;
[0036] Based on the joint parameters of the physical fields, the coupling correlation curve relationship between the various physical fields is obtained, and a nonlinear regression equation is constructed based on the coupling correlation curve relationship.
[0037] Based on the coupling correlation curve relationship, a decoupling model framework is constructed, and an intelligent decoupling model is generated by combining the nonlinear regression equation and the decoupling model framework.
[0038] Preferably, the step of obtaining the dynamic coupling parameters between the capacitor's capacitance value, internal resistance value, leakage current, and polarization voltage during the detection process, and substituting these dynamic coupling parameters into the intelligent decoupling model for decoupling to obtain the decoupled capacitance value, specifically includes:
[0039] The dynamic coupling parameters between the capacitance value, internal resistance value, leakage current, and polarization voltage of the capacitor are obtained during the detection process.
[0040] The dynamic coupling parameters are substituted into the intelligent decoupling model, and the intelligent decoupling model extracts the dynamic coupling changes of the dynamic coupling parameters;
[0041] Based on the dynamic coupling change, the coupling change trend is obtained, and the dynamic coupling parameters are decomposed by reverse coupling based on the coupling change trend to generate the initial capacity value and the coupling capacity compensation coefficient.
[0042] The initial capacity value is coupled and compensated according to the coupling capacity compensation coefficient to obtain the decoupling capacity value.
[0043] Preferably, the step of performing coefficient compensation on the decoupling capacity value based on the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacity value specifically includes:
[0044] The temperature coefficient compensation and the aging coefficient compensation are coupled and correlated to generate an initial coupling coefficient compensation.
[0045] Obtain the coupling relationship between temperature and aging in the capacitor, and generate coupling adjustment parameters based on the coupling relationship;
[0046] The initial coupling coefficient compensation is adjusted according to the coupling adjustment parameters to generate a joint coupling coefficient compensation. The decoupling capacity value is then compensated according to the joint coupling coefficient compensation to generate a target capacity value.
[0047] Secondly, this application provides an automatic capacitance detection method for new energy capacitors, the method comprising:
[0048] A gradient temperature environment is established, and the capacitance of the capacitor is measured according to the gradient temperature environment to obtain the capacitance change law under different temperature conditions. A capacitance-temperature mapping model is established based on the capacitance change law.
[0049] The surface temperature distribution of the capacitor is obtained during the detection process, and a temperature coefficient compensation is generated based on the capacitance-temperature mapping model and the surface temperature distribution.
[0050] Acquire charging and discharging data of the capacitor during the detection process, obtain charging and discharging curve characteristics based on the charging and discharging data, and capture the voltage plateau slope change and relaxation time shift based on the charging and discharging curve characteristics.
[0051] The electrode aging parameters of the capacitor are obtained based on the change in the slope of the voltage platform and the relaxation time offset, and the aging coefficient compensation for capacity detection is generated based on the electrode aging parameters.
[0052] The system acquires voltage, current, temperature, and frequency response data of a capacitor during charging and discharging. Based on the voltage, current, temperature, and frequency response data, it generates multi-physics joint parameters, constructs a nonlinear regression equation based on the multi-physics joint parameters, and builds an intelligent decoupling model based on the nonlinear regression equation.
[0053] The dynamic coupling parameters between the capacitance value, internal resistance value, leakage current and polarization voltage of the capacitor are obtained during the detection process. The dynamic coupling parameters are substituted into the intelligent decoupling model to decouple the capacitor and obtain the decoupled capacitance value.
[0054] The decoupling capacity value is compensated by the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacity value.
[0055] In summary, this application includes at least one of the following beneficial technical effects:
[0056] By establishing a gradient temperature environment, basic capacity testing of capacitors is performed to obtain the capacity variation law under different temperature conditions. Then, a capacity-temperature mapping model is established based on the capacity variation law. The surface temperature distribution of the capacitor during the testing process is collected, and a temperature coefficient compensation is generated based on the surface temperature distribution and the capacity-temperature mapping model. By acquiring the charge and discharge data of the capacitor during the testing process, the characteristics of the charge and discharge curves are obtained. Based on the characteristics of the charge and discharge curves, the voltage plateau slope change and relaxation time shift are captured. The electrode aging parameters of the capacitor are obtained based on the voltage plateau slope change and relaxation time shift, and an aging coefficient compensation is generated based on the capacitor aging parameters. Furthermore, voltage, current, temperature, and frequency response data of the capacitor during the charge and discharge process are acquired to generate multi-physics joint parameters. A nonlinear regression equation is constructed based on the multi-physics joint parameters, and an intelligent decoupling model is built using the nonlinear regression equation. The dynamic coupling parameters between the capacitor's capacity and various parameters during the testing process are acquired. These dynamic coupling parameters are substituted into the intelligent decoupling model for decoupling to obtain the decoupled capacity value. The decoupled capacity value is compensated based on temperature coefficient compensation and aging coefficient compensation to obtain the target capacity value. This improves the accuracy of capacity testing for new energy capacitors. Attached Figure Description
[0057] Figure 1 This is a block diagram of an automatic capacity detection device for new energy capacitors provided in this application.
[0058] Figure 2 This is a flowchart of the steps of an automatic capacity detection method for new energy capacitors provided in this application.
[0059] Explanation of reference numerals in the attached diagram: 1. Temperature analysis module; 2. Temperature compensation module; 3. Aging analysis module; 4. Aging compensation module; 5. Coupling analysis module; 6. Parameter decoupling module; 7. Capacity calculation module. Detailed Implementation
[0060] The following combination Figures 1-2 This application will be described in further detail, but the embodiments of the present invention are not limited thereto.
[0061] This application discloses an automatic capacity detection device and method for new energy capacitors.
[0062] In this embodiment, an automatic capacity detection device for new energy capacitors is provided, the device comprising:
[0063] Temperature Analysis Module 1: Used to establish a gradient temperature environment, perform capacitance detection on the capacitor based on the gradient temperature environment, obtain the capacitance change law under different temperature conditions, and establish a capacitance-temperature mapping model based on the capacitance change law;
[0064] Temperature compensation module 2: used to acquire the surface temperature distribution of the capacitor during the detection process, and generate temperature coefficient compensation based on the capacitance-temperature mapping model and the surface temperature distribution;
[0065] Aging Analysis Module 3: Used to acquire the charging and discharging data of the capacitor during the testing process, obtain the charging and discharging curve characteristics based on the charging and discharging data, and capture the voltage plateau slope change and relaxation time shift based on the charging and discharging curve characteristics.
[0066] Aging Compensation Module 4: Used to obtain the electrode aging parameters of the capacitor based on the change in voltage plateau slope and relaxation time offset, and to generate an aging coefficient compensation for capacitance detection based on the electrode aging parameters.
[0067] Coupling Analysis Module 5: Used to acquire voltage, current, temperature and frequency response data of capacitor during charging and discharging; generate multi-physics joint parameters based on voltage, current and temperature data and frequency response; construct nonlinear regression equation based on multi-physics joint parameters; and construct intelligent decoupling model based on nonlinear regression equation.
[0068] Parameter decoupling module 6: Used to obtain the dynamic coupling parameters between the capacitance value, internal resistance value, leakage current and polarization voltage of the capacitor during the detection process, and substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling to obtain the decoupled capacitance value;
[0069] Capacity Calculation Module 7: Used to perform coefficient compensation on the decoupled capacity value based on temperature coefficient compensation and aging coefficient compensation to obtain the target capacity value.
[0070] It should be noted that the above modules are only the basic modules of this embodiment. In the specific implementation process, some modules may be added, reduced or modified as appropriate without affecting the overall implementation effect.
[0071] The steps for measuring the capacitance of a capacitor under a gradient temperature environment to obtain the capacitance variation pattern under different temperature conditions, and establishing a capacitance-temperature mapping model based on the capacitance variation pattern, are as follows:
[0072] Based on the gradient temperature environment, the capacitor is placed in different preset temperature environments to perform initial capacity testing and obtain multiple initial capacity data.
[0073] Based on the gradient temperature environment and multiple initial capacity data, a temperature-capacity correspondence table is generated, and the capacity change pattern is obtained from the temperature-capacity correspondence table.
[0074] Based on the capacity change pattern, a capacity-temperature curve is established, and the curve change characteristics are obtained from the capacity-temperature curve.
[0075] Based on the curve variation characteristics, the variation characteristics and corresponding relationships of capacity with temperature are obtained, and a capacity-temperature mapping model is established based on the variation characteristics and corresponding relationships.
[0076] In practice, taking a two-year-old new energy capacitor from a certain brand as an example, according to the requirements of the gradient temperature environment setting, the capacitor was placed in constant temperature chambers at -20°C, 0°C, 25°C, 40°C, and 60°C sequentially. Each temperature environment was maintained for 30 minutes. After the capacitor temperature stabilized, initial capacity testing was performed using a standard capacity testing device. The measured capacity was 980mAh at -20°C, 995mAh at 0°C, 1020mAh at 25°C, 1010mAh at 40°C, and 990mAh at 60°C. A temperature-capacity correspondence table was generated based on these data, showing that the capacity first increases and then decreases with increasing temperature. A capacity-temperature curve was plotted based on this table, revealing that the curve shows an increasing slope of 0.5mAh / °C in the 0°C to 40°C range, and a decreasing slope of -0.25mAh / °C in the 40°C to 60°C range. The slope changes between these two intervals were extracted as curve features to establish a mapping relationship between capacity and temperature: when the temperature is below 40°C, the capacity increases by 0.5 mAh for every 1°C increase, and when the temperature is above 40°C, the capacity decreases by 0.25 mAh for every 1°C increase.
[0077] The steps for obtaining the surface temperature distribution of the capacitor during the testing process and generating temperature coefficient compensation based on the capacitance-temperature mapping model and the surface temperature distribution are as follows:
[0078] The surface temperature distribution of the capacitor is obtained during the detection process, and a temperature distribution display map is generated based on the surface temperature distribution.
[0079] Based on the temperature distribution map, the high-temperature and low-temperature regions on the capacitor surface, as well as the high-temperature and low-temperature values, are extracted.
[0080] Based on the high-temperature and low-temperature regions, the corresponding regional temperature influences are obtained. The high-temperature and low-temperature values are then weighted according to the regional influences to obtain weighted high-temperature and weighted low-temperature values.
[0081] Based on the weighted high temperature and weighted low temperature values, a weighted average temperature value is generated. The weighted average temperature value is then substituted into the capacity-temperature mapping model to generate a temperature coefficient compensation.
[0082] In application, taking a two-year-old new energy capacitor from a certain brand as an example, an infrared thermal imager was used to scan its surface during the testing process, generating a surface temperature distribution map showing that the temperature of the left half of the capacitor was 48°C (high temperature area) and the temperature of the right half was 32°C (low temperature area). The high temperature area accounted for 30% of the area, and the low temperature area accounted for 70%. According to the equipment technical manual, the influence weight of the high temperature area on capacity detection is 0.7, and the weight of the low temperature area is 0.3. The weighted high temperature value was calculated as 48°C × 0.7 = 33.6, and the weighted low temperature value was calculated as 32°C × 0.3 = 9.6, resulting in a weighted average temperature value of 43.2°C. Substituting this temperature value into the capacity-temperature mapping model established in claim 2, the temperature coefficient compensation was calculated: at 43.2°C, the theoretical capacity should be 1020mAh - (43.2 - 40) × 0.25 = 1019.2mAh, while the measured value was 1015mAh, resulting in a temperature compensation coefficient of +4.2mAh.
[0083] The steps for obtaining charge / discharge curve characteristics based on charge / discharge data, and then capturing the voltage plateau slope change and relaxation time shift based on these characteristics, are as follows:
[0084] Charge and discharge data curves are generated based on charge and discharge data, and features are extracted from the charge and discharge data curves to obtain charge and discharge curve features.
[0085] Based on the characteristics of the charge and discharge curves, the voltage plateau data and relaxation time data of the capacitor are captured.
[0086] The voltage plateau slope data is obtained from the voltage plateau data, and the voltage plateau slope change is obtained from the voltage plateau slope data.
[0087] The relaxation time offset is obtained by extracting the relaxation time offset from the relaxation time data.
[0088] In practical application, taking a new energy capacitor from a certain brand that has been used for two years as an example, during a 1A constant current charge-discharge test, it was recorded that the voltage took 120 seconds to rise from 0V to 2.5V during the charging phase, and 95 seconds to drop from 2.5V to 1.8V during the discharging phase. After generating the charge-discharge curves, the slope of the charging voltage plateau (2.3V-2.5V range) was 0.016V / s, a 20% decrease compared to the standard value of 0.02V / s; the slope of the discharging voltage plateau (2.1V-1.8V range) was -0.028V / s, a 12% deviation compared to the standard value of -0.025V / s. Simultaneously, the relaxation time after charging (the time required for the voltage to drop back to 2.0V) was detected to be 45ms, a 50% increase compared to the standard value of 30ms; the discharge relaxation time was 38ms, a 52% increase compared to the standard value of 25ms. These data collectively indicate that the capacitor suffers from aging of the electrode active material and deterioration of the electrolyte.
[0089] The steps for obtaining capacitor electrode aging parameters based on voltage plateau slope changes and relaxation time shifts, and generating aging coefficient compensation for capacitance testing based on these parameters, are as follows:
[0090] The aging degree of the capacitor is identified by the voltage plateau slope data, and the first aging parameter of the capacitor is obtained.
[0091] The aging degree of the capacitor is identified by a second parameter based on the relaxation time offset, and the second aging parameter of the capacitor is obtained.
[0092] The first identification aging parameter and the second identification aging parameter are integrated to obtain the electrode aging parameters of the capacitor.
[0093] Based on the electrode aging parameters, the aging capacity impact value of electrode aging on capacitor capacity is obtained, and an aging coefficient compensation is generated based on the aging capacity impact value.
[0094] In application, taking a new energy capacitor from a certain brand that has been used for two years as an example, based on the data showing a 20% decrease in the charging voltage plateau slope, and matching the aging parameter reference table, the electrode active material loss rate is determined to be 18%; based on the data showing a 50% increase in discharge relaxation time, the electrolyte conductivity is determined to have decreased by 22%. Integrating these two types of parameters, the comprehensive aging parameter is calculated as (18% + 22%) / 2 = 20%. According to the aging parameter-capacity loss reference table, the capacity loss corresponding to the 20% aging parameter is 15% of the rated capacity (1000mAh), i.e., 150mAh. An aging coefficient compensation of +150mAh is generated to correct the detected capacity value.
[0095] The steps involved in generating multi-physics joint parameters based on voltage, current, temperature, and frequency response data, constructing a nonlinear regression equation based on these parameters, and then constructing an intelligent decoupling model based on the nonlinear regression equation are as follows:
[0096] Voltage physical field parameters are generated based on voltage data, current physical field parameters are generated based on current data, temperature physical field parameters are generated based on temperature data, and frequency physical field parameters are generated based on frequency response.
[0097] Combine the physical field parameters of voltage, current, temperature, and frequency to generate joint physical field parameters;
[0098] Based on the joint parameters of the physical fields, the coupling correlation curve relationship between the various physical fields is obtained, and a nonlinear regression equation is constructed based on the coupling correlation curve relationship.
[0099] A decoupling model framework is constructed based on the coupling correlation curve relationship, and an intelligent decoupling model is generated by combining the nonlinear regression equation and the decoupling model framework.
[0100] In application, taking a new energy capacitor from a certain brand that has been used for two years as an example, the following data was collected during the charging and discharging process: voltage fluctuation range 2.8V-3.2V (generated voltage field parameter V=3.0±0.2V), current ripple coefficient 0.15 (generated current field parameter I=1.0±0.15A), surface hot spot temperature 52°C (temperature field parameter T=52°C), and frequency response curve phase angle shift of 8° at 1kHz (frequency field parameter F=1kHz@-8°). These four parameters were input into a multiphysics coupling model, and the voltage-temperature coupling coefficient was calculated to be 0.32, and the current-frequency coupling coefficient to be -0.18. An equation was established using polynomial regression: Capacity = 1020 - 0.32*(T-25) + 0.18*(F-1kHz). A three-layer neural network decoupling model framework was constructed, with the input layer containing four physical field parameters, the hidden layer having six nodes, and the output layer generating a decoupling capacity benchmark value.
[0101] The dynamic coupling parameters between the capacitor's capacitance, internal resistance, leakage current, and polarization voltage during the capacitor testing process are used to decouple the capacitor by substituting these dynamic coupling parameters into an intelligent decoupling model, resulting in the decoupled capacitance value. The specific steps are as follows:
[0102] The dynamic coupling parameters between the capacitance value, internal resistance value, leakage current, and polarization voltage of the capacitor are obtained during the detection process.
[0103] By substituting the dynamic coupling parameters into the intelligent decoupling model, the intelligent decoupling model extracts the dynamic coupling changes of the dynamic coupling parameters.
[0104] The coupling change trend is obtained based on the dynamic coupling change, and the dynamic coupling parameters are decomposed by reverse coupling based on the coupling change trend to generate the initial capacity value and the coupling capacity compensation coefficient.
[0105] The initial capacity value is coupled and compensated based on the coupling capacity compensation coefficient to obtain the decoupling capacity value.
[0106] In application, taking a new energy capacitor from a certain brand that has been used for two years as an example, during the testing process, it was found that when the internal resistance increased from 50mΩ to 65mΩ, the displayed capacity value dropped from 980mAh to 920mAh (coupling coefficient -4mAh / mΩ); when the leakage current increased from 10μA to 25μA, the displayed capacity value decreased by 30mAh (coupling coefficient -2mAh / μA). These dynamic coupling parameters were input into an intelligent decoupling model. The model identified that changes in internal resistance mainly affect charging efficiency (weight 0.6), while leakage current affects self-discharge (weight 0.4). Through reverse calculation, the initial capacity value of 980mAh was separated, generating coupling compensation coefficients: internal resistance compensation + 4 × 15 = 60mAh, leakage current compensation + 2 × 15 = 30mAh, and the final decoupled capacity value = 920 + 60 + 30 = 1010mAh.
[0107] The steps for compensating the decoupling capacity value based on temperature coefficient compensation and aging coefficient compensation to obtain the target capacity value are as follows:
[0108] The temperature coefficient compensation and aging coefficient compensation are coupled and correlated to generate the initial coupling coefficient compensation.
[0109] Obtain the coupling relationship between temperature and aging in the capacitor, and generate coupling adjustment parameters based on the coupling relationship;
[0110] The initial coupling coefficient compensation is adjusted based on the coupling adjustment parameters to generate a joint coupling coefficient compensation. The decoupling capacity value is then compensated based on the joint coupling coefficient compensation to generate the target capacity value.
[0111] In application, taking a new energy capacitor from a certain brand that has been used for two years as an example, the temperature compensation coefficient of +4.2mAh and the aging compensation of +150mAh are coupled, with an initial compensation value of 154.2mAh. Testing revealed that the electrode aging rate accelerates with increasing temperature, establishing a temperature-aging correlation factor: the aging rate increases by 0.5% for every 1°C increase. At a testing temperature of 43.2°C, the aging compensation is adjusted to 150 × [1 + 0.5% × (43.2 - 25)] = 150 × 1.091 = 163.65mAh. The combined compensation value is corrected to 4.2 + 163.65 = 167.85mAh. Compensating for the decoupled capacity value of 1010mAh yields a target capacity value of 1010 + 167.85 = 1177.85mAh, rounded to 1180mAh, which is the corrected actual capacity value.
[0112] This invention provides an automatic capacity detection method for new energy capacitors, using an automatic capacity detection device for new energy capacitors as described above. The method includes the following:
[0113] S100: Establish a gradient temperature environment, perform capacitance detection on the capacitor based on the gradient temperature environment, obtain the capacitance change law under different temperature conditions, and establish a capacitance-temperature mapping model based on the capacitance change law.
[0114] S200: Acquire the surface temperature distribution of the capacitor during the detection process, and generate temperature coefficient compensation based on the capacitance-temperature mapping model and the surface temperature distribution;
[0115] S300: Acquires the charging and discharging data of the capacitor during the detection process, obtains the charging and discharging curve characteristics based on the charging and discharging data, and captures the voltage plateau slope change and relaxation time offset based on the charging and discharging curve characteristics.
[0116] S400: Obtain the electrode aging parameters of the capacitor based on the change in voltage plateau slope and relaxation time offset, and generate the aging coefficient compensation for capacitance detection based on the electrode aging parameters.
[0117] S500: Acquires voltage, current, temperature and frequency response data of capacitors during charging and discharging; generates multi-physics joint parameters based on voltage, current and temperature data and frequency response; constructs nonlinear regression equations based on multi-physics joint parameters; and constructs intelligent decoupling models based on nonlinear regression equations.
[0118] S600: Acquires the dynamic coupling parameters between the capacitance value, internal resistance value, leakage current and polarization voltage of the capacitor during the detection process, substitutes the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtains the decoupled capacitance value.
[0119] S700: The decoupling capacity value is compensated by temperature coefficient compensation and aging coefficient compensation to obtain the target capacity value.
[0120] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.
Claims
1. A capacity automatic detection device for a new energy capacitor, characterized in that, The method comprises the following steps: a temperature analysis module: for establishing a gradient temperature environment, detecting the capacity of the capacitor according to the gradient temperature environment, obtaining the capacity variation law under different temperature conditions, and establishing a capacity-temperature mapping model according to the capacity variation law; a temperature compensation module: for obtaining the surface temperature distribution of the capacitor during the detection process, generating temperature coefficient compensation according to the capacity-temperature mapping model and the surface temperature distribution; an aging analysis module: for obtaining the charge and discharge data of the capacitor during the detection process, obtaining the charge and discharge curve characteristics according to the charge and discharge data, and capturing the voltage platform slope change and relaxation time offset according to the charge and discharge curve characteristics; an aging compensation module: for obtaining the electrode aging parameters of the capacitor according to the voltage platform slope change and the relaxation time offset, and generating aging coefficient compensation for capacity detection according to the electrode aging parameters; a coupling analysis module: for obtaining the voltage data, current data, temperature data and frequency response of the capacitor during the charge and discharge process, generating multi-physical field joint parameters according to the voltage data, current data, temperature data and frequency response, constructing a nonlinear regression equation according to the multi-physical field joint parameters, and constructing an intelligent decoupling model according to the nonlinear regression equation; a parameter decoupling module: for obtaining the dynamic coupling parameters between the capacity value and the internal resistance value, leakage current and polarization voltage of the capacitor during the detection process, and decoupling the dynamic coupling parameters in the intelligent decoupling model to obtain the decoupled capacity value; a capacity calculation module: for performing coefficient compensation on the decoupled capacity value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacity value.
2. The capacity automatic detection device for new energy capacitor according to claim 1, characterized in that, The step of detecting the capacity of the capacitor according to the gradient temperature environment to obtain the capacity variation law under different temperature conditions, and establishing a capacity-temperature mapping model according to the capacity variation law, is specifically: According to the gradient temperature environment, the capacitor is placed in a preset different temperature environment for initial capacity detection to obtain a plurality of initial capacity data; According to the gradient temperature environment and a plurality of initial capacity data, a temperature-capacity correspondence table is generated, and a capacity variation law is obtained according to the temperature-capacity correspondence table; Based on the capacity variation law, a capacity-temperature curve is established, and a curve variation characteristic is obtained according to the capacity-temperature curve; According to the curve variation characteristic, a variation characteristic and a variation corresponding relationship of the capacity change with temperature are obtained, and a capacity-temperature mapping model is established according to the variation characteristic and the variation corresponding relationship.
3. The capacity automatic detection device for new energy capacitor according to claim 2, characterized in that, The step of obtaining the surface temperature distribution of the capacitor during the detection process, and generating temperature coefficient compensation according to the capacity-temperature mapping model and the surface temperature distribution, is specifically: Obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature distribution display diagram according to the surface temperature distribution; Based on the temperature distribution display diagram, the high temperature area and the low temperature area of the capacitor surface, as well as the high temperature value and the low temperature value, are extracted; Based on the high-temperature region and the low-temperature region, a corresponding region temperature influence is obtained, and the high-temperature value and the low-temperature value are weighted according to the region influence to obtain a weighted high-temperature value and a weighted low-temperature value; Based on the weighted high-temperature value and the weighted low-temperature value, a weighted average temperature value is generated, and the weighted average temperature value is substituted into the capacity-temperature mapping model to generate a temperature coefficient compensation.
4. The capacity automatic detection device for new energy capacitor according to claim 1, characterized in that, According to the charge-discharge data, a charge-discharge curve feature is obtained, and the steps of capturing the voltage platform slope change and the relaxation time offset according to the charge-discharge curve feature are as follows: According to the charge-discharge data, a charge-discharge data curve is generated, and a feature extraction is performed on the charge-discharge data curve to obtain a charge-discharge curve feature; According to the charge-discharge curve feature, voltage platform data and relaxation time data of the capacitor are captured; According to the voltage platform data, voltage platform slope data is obtained, and according to the voltage platform slope data, a voltage platform slope change is obtained; Based on the relaxation time data, the offset of the relaxation time is extracted to obtain a relaxation time offset.
5. The capacity automatic detection device for new energy capacitor according to claim 4, characterized in that, According to the voltage platform slope change and the relaxation time offset, an electrode aging parameter of the capacitor is obtained, and the steps of generating an aging coefficient compensation for capacity detection according to the electrode aging parameter are as follows: According to the voltage platform slope data, a first parameter identification of the aging degree of the capacitor is performed to obtain a first identified aging parameter of the capacitor; According to the relaxation time offset, a second parameter identification of the aging degree of the capacitor is performed to obtain a second identified aging parameter of the capacitor; The first identified aging parameter and the second identified aging parameter are integrated to obtain an electrode aging parameter of the capacitor; According to the electrode aging parameter, an aging capacity influence value of the influence of electrode aging on the capacity of the capacitor is obtained, and an aging coefficient compensation is generated according to the aging capacity influence value.
6. The capacity automatic detection device for new energy capacitor according to claim 1, characterized in that, According to the voltage data, the current data, the temperature data, and the frequency response, a multi-physical field joint parameter is generated, a nonlinear regression equation is constructed according to the multi-physical field joint parameter, and an intelligent decoupling model is constructed according to the nonlinear regression equation. According to the voltage data, a voltage physical field parameter is generated, according to the current data, a current physical field parameter is generated, according to the temperature data, a temperature physical field parameter is generated, and according to the frequency response, a frequency physical field parameter is generated; The voltage physical field parameter, the current physical field parameter, the temperature physical field parameter, and the frequency physical field parameter are combined to generate a physical field joint parameter; According to the physical field joint parameter, a coupling correlation curve relationship between each physical field is obtained, and a nonlinear regression equation is constructed according to the coupling correlation curve relationship; Based on the coupling correlation curve relationship, a decoupling model framework is constructed, and an intelligent decoupling model is generated by combining the nonlinear regression equation and the decoupling model framework.
7. The capacity automatic detection device for new energy capacitor according to claim 6, characterized in that, The steps of acquiring the dynamic coupling parameters between the capacitor's capacitance, internal resistance, leakage current, and polarization voltage during the detection process, and substituting these dynamic coupling parameters into the intelligent decoupling model for decoupling to obtain the decoupled capacitance value are as follows: The dynamic coupling parameters between the capacitance value, internal resistance value, leakage current, and polarization voltage of the capacitor are obtained during the detection process. The dynamic coupling parameters are substituted into the intelligent decoupling model, and the intelligent decoupling model extracts the dynamic coupling changes of the dynamic coupling parameters; Based on the dynamic coupling change, the coupling change trend is obtained, and the dynamic coupling parameters are decomposed by reverse coupling based on the coupling change trend to generate the initial capacity value and the coupling capacity compensation coefficient. The initial capacity value is coupled and compensated according to the coupling capacity compensation coefficient to obtain the decoupling capacity value.
8. The capacity automatic detection device for new energy capacitor according to claim 7, characterized in that, The step of compensating the decoupling capacity value based on the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacity value is as follows: The temperature coefficient compensation and the aging coefficient compensation are coupled and correlated to generate an initial coupling coefficient compensation. Obtain the coupling relationship between temperature and aging in the capacitor, and generate coupling adjustment parameters based on the coupling relationship; The initial coupling coefficient compensation is adjusted according to the coupling adjustment parameters to generate a joint coupling coefficient compensation. The decoupling capacity value is then compensated according to the joint coupling coefficient compensation to generate a target capacity value.
9. A method for automatically detecting the capacity of a new energy capacitor, wherein the method uses the device for automatically detecting the capacity of a new energy capacitor according to any one of claims 1-8, characterized in that, The method includes: A gradient temperature environment is established, and the capacitance of the capacitor is measured according to the gradient temperature environment to obtain the capacitance change law under different temperature conditions. A capacitance-temperature mapping model is established based on the capacitance change law. The surface temperature distribution of the capacitor is obtained during the detection process, and a temperature coefficient compensation is generated based on the capacitance-temperature mapping model and the surface temperature distribution. Acquire charging and discharging data of the capacitor during the detection process, obtain charging and discharging curve characteristics based on the charging and discharging data, and capture the voltage plateau slope change and relaxation time shift based on the charging and discharging curve characteristics. The electrode aging parameters of the capacitor are obtained based on the change in the slope of the voltage platform and the relaxation time offset, and the aging coefficient compensation for capacity detection is generated based on the electrode aging parameters. The system acquires voltage, current, temperature, and frequency response data of a capacitor during charging and discharging. Based on the voltage, current, temperature, and frequency response data, it generates multi-physics joint parameters, constructs a nonlinear regression equation based on the multi-physics joint parameters, and builds an intelligent decoupling model based on the nonlinear regression equation. The dynamic coupling parameters between the capacitance value, internal resistance value, leakage current and polarization voltage of the capacitor are obtained during the detection process. The dynamic coupling parameters are substituted into the intelligent decoupling model to decouple the capacitor and obtain the decoupled capacitance value. The decoupling capacity value is compensated by the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacity value.
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